Title :
Stable predictive control based on recurrent fuzzy neural networks
Author :
Lu, Chi-Huang ; Liu, Chi-Ming ; Charng, Yuan-Hai
Author_Institution :
Dept. of Electr. Eng., Hsiuping Inst. of Technol., Taichung, Taiwan
Abstract :
This paper presents a design method for stable predictive control (SPC) of nonlinear discrete-time system via recurrent fuzzy neural networks (RFNNs). An RFNN-based predictive control law is derived based on the minimization of a modified predictive performance criterion. Two theorems are presented for the stability and steady-state performance of the closed-loop systems. The results from numerical simulations show that the proposed SPC method is capable of controlling nonlinear systems with satisfactory performance under setpoint and disturbance changes.
Keywords :
closed loop systems; control system synthesis; discrete time systems; fuzzy neural nets; minimisation; nonlinear control systems; numerical analysis; predictive control; recurrent neural nets; stability; closed loop systems; minimization; modified predictive performance criterion; nonlinear discrete time system; numerical simulations; recurrent fuzzy neural networks; stability; stable predictive control design method; Artificial neural networks; Control systems; Fuzzy control; Fuzzy neural networks; Nonlinear systems; Predictive control; Predictive models; Generalized predictive control; nonlinear discrete-time system; recurrent fuzzy neural network;
Conference_Titel :
Control Conference (ASCC), 2011 8th Asian
Conference_Location :
Kaohsiung
Print_ISBN :
978-1-61284-487-9
Electronic_ISBN :
978-89-956056-4-6
